Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00107
Johanna Hopane, B. Gatsheni
Flight departure delays are a major problem at OR Tambo International airport (ORTIA) located in Johannesburg in South Africa. These delays are more pronounced at the beginning and end of the month. Flight delays at ORTIA do impact negatively on business, on job opportunities and on tourists. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. Cross-validation (CV) was used for evaluating the models. The best prediction model was selected by using a confusion matrix and the ROC curve. The results show that the models constructed using data and the Decision Trees is suited for flight departure delay prediction as it gave the best prediction of 67.144%. The implications of the model is that travellers wishing to travel from ORTIA can foretell the flight departure delays using the tool. The tool will allow the travellers to enter variables such as month, week of month, day of week and time of day.
{"title":"A Computational Intelligence-Based Prediction Model for Flight Departure Delays","authors":"Johanna Hopane, B. Gatsheni","doi":"10.1109/CSCI49370.2019.00107","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00107","url":null,"abstract":"Flight departure delays are a major problem at OR Tambo International airport (ORTIA) located in Johannesburg in South Africa. These delays are more pronounced at the beginning and end of the month. Flight delays at ORTIA do impact negatively on business, on job opportunities and on tourists. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. Cross-validation (CV) was used for evaluating the models. The best prediction model was selected by using a confusion matrix and the ROC curve. The results show that the models constructed using data and the Decision Trees is suited for flight departure delay prediction as it gave the best prediction of 67.144%. The implications of the model is that travellers wishing to travel from ORTIA can foretell the flight departure delays using the tool. The tool will allow the travellers to enter variables such as month, week of month, day of week and time of day.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00298
Dong h. Lee, Kong-Rae Lee, J. H. Lee
we will introduce a new program so-called TVA(Tech-Venture Academy) for the setup of a role model, and cultivation of outstanding enterprise innovation experts and investigate the performance of its program to overcome the crisis faced by the Korean manufacturing industry due to the global economic recession and the dumping of companies in developing countries, and to lead the role of regional industry promotion and also, to introduce innovation management experts and industry re-creation because DGIST should play as a science and technology specialization and leading university located in Daegu of South Korea.
{"title":"Development of Innovative Education Program for Tech-Oriented Industrial Structure Improvement of Local Industries by Fostering Start-Up Companies: TVA (Tech-Venture Academy) Program","authors":"Dong h. Lee, Kong-Rae Lee, J. H. Lee","doi":"10.1109/CSCI49370.2019.00298","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00298","url":null,"abstract":"we will introduce a new program so-called TVA(Tech-Venture Academy) for the setup of a role model, and cultivation of outstanding enterprise innovation experts and investigate the performance of its program to overcome the crisis faced by the Korean manufacturing industry due to the global economic recession and the dumping of companies in developing countries, and to lead the role of regional industry promotion and also, to introduce innovation management experts and industry re-creation because DGIST should play as a science and technology specialization and leading university located in Daegu of South Korea.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124587353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00168
C. Beaton
Distance learning has brought phenomenal changes to the educational playing field. In higher education, variances of distance learning can mean blended learning, flipped classrooms, or video modules/components. While distance learning results in no physical in-person interaction, online supplements physical interpersonal interactions. This paper will focus on distance learning in relation to people with disabilities, demonstrating the challenges that are faced with providing access to learners.
{"title":"Distance Learning as a Levelling Tool for People with Disabilities","authors":"C. Beaton","doi":"10.1109/CSCI49370.2019.00168","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00168","url":null,"abstract":"Distance learning has brought phenomenal changes to the educational playing field. In higher education, variances of distance learning can mean blended learning, flipped classrooms, or video modules/components. While distance learning results in no physical in-person interaction, online supplements physical interpersonal interactions. This paper will focus on distance learning in relation to people with disabilities, demonstrating the challenges that are faced with providing access to learners.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130166774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00128
P. Acevedo, M. Vazquez
In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.
{"title":"Classification of Tumors in Breast Echography Using a SVM Algorithm","authors":"P. Acevedo, M. Vazquez","doi":"10.1109/CSCI49370.2019.00128","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00128","url":null,"abstract":"In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128679603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00070
Pablo Rivas, Chelsi Chelsi, Nishit Nishit, Laharika Ravula
Advances in machine learning are making possible the interaction between humans and machines, coming closer to passing the Turing test. Chatbots, specifically, are a technology that uses the latest advances in natural language processing and machine learning to understand text and produce text in response to input. While this is an important achievement today, we must consider specific challenges that chatbot deployments might pose. This paper looks back to a historical event that took place in 2016 with the purpose of extracting important, memorable, lessons. The study suggests that certain assumptions with respect to societal values are of paramount importance and need to be considered carefully along with a proper platform selection.
{"title":"Application-Agnostic Chatbot Deployment Considerations: A Case Study","authors":"Pablo Rivas, Chelsi Chelsi, Nishit Nishit, Laharika Ravula","doi":"10.1109/CSCI49370.2019.00070","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00070","url":null,"abstract":"Advances in machine learning are making possible the interaction between humans and machines, coming closer to passing the Turing test. Chatbots, specifically, are a technology that uses the latest advances in natural language processing and machine learning to understand text and produce text in response to input. While this is an important achievement today, we must consider specific challenges that chatbot deployments might pose. This paper looks back to a historical event that took place in 2016 with the purpose of extracting important, memorable, lessons. The study suggests that certain assumptions with respect to societal values are of paramount importance and need to be considered carefully along with a proper platform selection.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128837658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00076
N. Rowe, Charles Knight
Simulations can produce large quantities of data. To reason about the results of simulations, machine-learning methods can be helpful. We explored a case-based reasoning approach to summarizing the results of a probabilistic simulation of naval combat involving missiles. We used a tree structure to index the data and showed that it gave good accuracy in estimating the results of this simulation with new parameters. We are now extending these ideas to a more complex military simulation.
{"title":"Case-Based Reasoning for Summarizing Simulation Results","authors":"N. Rowe, Charles Knight","doi":"10.1109/CSCI49370.2019.00076","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00076","url":null,"abstract":"Simulations can produce large quantities of data. To reason about the results of simulations, machine-learning methods can be helpful. We explored a case-based reasoning approach to summarizing the results of a probabilistic simulation of naval combat involving missiles. We used a tree structure to index the data and showed that it gave good accuracy in estimating the results of this simulation with new parameters. We are now extending these ideas to a more complex military simulation.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00165
Srivalli Dingari, N. Mahapatra
Choosing the most suitable college courses can be a time-consuming task, given the number of sources from which students need to pull the information regarding degree requirements. In addition, given the limited time and interaction between advisor and student, substantial effort needs to be put in to find a proper path towards graduation. To bridge the gap, a number of degree auditing software systems emerged and evolved, making it easier for students to have a convenient road map and plan their graduation. This study surveys the features of popular degree auditing systems and two research papers, one from Cornell University and the other from Texas State University, on the design and structure of a degree auditing system.
{"title":"A Short Survey of Degree Auditing Systems","authors":"Srivalli Dingari, N. Mahapatra","doi":"10.1109/CSCI49370.2019.00165","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00165","url":null,"abstract":"Choosing the most suitable college courses can be a time-consuming task, given the number of sources from which students need to pull the information regarding degree requirements. In addition, given the limited time and interaction between advisor and student, substantial effort needs to be put in to find a proper path towards graduation. To bridge the gap, a number of degree auditing software systems emerged and evolved, making it easier for students to have a convenient road map and plan their graduation. This study surveys the features of popular degree auditing systems and two research papers, one from Cornell University and the other from Texas State University, on the design and structure of a degree auditing system.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121178471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00118
Rommel Fernandes, Lei Huang, G. Vejarano
Research advancement of human-computer interaction (HCI) has recently been made to help post-stroke victims dealing with physiological problems such as speech impediments due to aphasia. This paper investigates different deep learning approaches used for non-audible speech recognition using electromyography (EMG) signals with a novel approach employing continuous wavelet transforms (CWT) and convolutional neural networks (CNNs). To compare its performance with other popular deep learning approaches, we collected facial surface EMG bio-signals from subjects with binary and multi-class labels, trained and tested four models, including a long-short term memory(LSTM) model, a bi-directional LSTM model, a 1-D CNN model, and our proposed CWT-CNN model. Experimental results show that our proposed approach performs better than the LSTM models, but is less efficient than the 1-D CNN model on our collected data set. In comparison with previous research, we gained insights on how to improve the performance of the model for binary and multi-class silent speech recognition.
{"title":"Non-Audible Speech Classification Using Deep Learning Approaches","authors":"Rommel Fernandes, Lei Huang, G. Vejarano","doi":"10.1109/CSCI49370.2019.00118","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00118","url":null,"abstract":"Research advancement of human-computer interaction (HCI) has recently been made to help post-stroke victims dealing with physiological problems such as speech impediments due to aphasia. This paper investigates different deep learning approaches used for non-audible speech recognition using electromyography (EMG) signals with a novel approach employing continuous wavelet transforms (CWT) and convolutional neural networks (CNNs). To compare its performance with other popular deep learning approaches, we collected facial surface EMG bio-signals from subjects with binary and multi-class labels, trained and tested four models, including a long-short term memory(LSTM) model, a bi-directional LSTM model, a 1-D CNN model, and our proposed CWT-CNN model. Experimental results show that our proposed approach performs better than the LSTM models, but is less efficient than the 1-D CNN model on our collected data set. In comparison with previous research, we gained insights on how to improve the performance of the model for binary and multi-class silent speech recognition.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00113
Adam Hennad, P. Cockett, L. McLauchlan, M. Mehrubeoglu
Volume computations are important for the characterization of three-dimensional (3D) objects. In the case of irregularly-shaped objects, volumetric analysis remains challenging due to the missing symmetry in the geometry. 3D scanners provide a solution for digitizing the shape of objects for 3D visualization; however, typical scanners do not provide detailed quantitative information which offers significant advantage in both research and development applications. In this work, tools and operations that utilize digital 3D data captured via a 3D structured-light scanner are investigated to develop algorithms that accurately model and compute the volume of non-uniform objects. Specifically, limpet seashells are utilized to develop the models for volumetric analysis and characterization using MATLAB programming toolboxes after the 3D scans are completed.
{"title":"Characterization of Irregularly-Shaped Objects Using 3D Structured Light Scanning","authors":"Adam Hennad, P. Cockett, L. McLauchlan, M. Mehrubeoglu","doi":"10.1109/CSCI49370.2019.00113","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00113","url":null,"abstract":"Volume computations are important for the characterization of three-dimensional (3D) objects. In the case of irregularly-shaped objects, volumetric analysis remains challenging due to the missing symmetry in the geometry. 3D scanners provide a solution for digitizing the shape of objects for 3D visualization; however, typical scanners do not provide detailed quantitative information which offers significant advantage in both research and development applications. In this work, tools and operations that utilize digital 3D data captured via a 3D structured-light scanner are investigated to develop algorithms that accurately model and compute the volume of non-uniform objects. Specifically, limpet seashells are utilized to develop the models for volumetric analysis and characterization using MATLAB programming toolboxes after the 3D scans are completed.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00061
Samuel Dixon, Raleigh Hansen, Wesley Deneke
We present a method of representing human activities as Probabilistic Context Free Grammars(PCFGs). Our method will allow these grammars to be learned from any source of data that describe sequences of human actions. We describe how representing human activities as PCFGs will allow them to be used for multiple proposed applications. The method proposed is interpretable such that the representation of an activity can be edited by a human annotator for further increase in performance. We also introduce a method of simulating realistic sequences of human actions, and describe how realistic noise is injected into this data. We propose methods of inducting grammars from this synthetic data and experiments to evaluate both the data and the ability of PCFGs to represent human activities.
{"title":"Probabilistic Grammar Induction for Long Term Human Activity Parsing","authors":"Samuel Dixon, Raleigh Hansen, Wesley Deneke","doi":"10.1109/CSCI49370.2019.00061","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00061","url":null,"abstract":"We present a method of representing human activities as Probabilistic Context Free Grammars(PCFGs). Our method will allow these grammars to be learned from any source of data that describe sequences of human actions. We describe how representing human activities as PCFGs will allow them to be used for multiple proposed applications. The method proposed is interpretable such that the representation of an activity can be edited by a human annotator for further increase in performance. We also introduce a method of simulating realistic sequences of human actions, and describe how realistic noise is injected into this data. We propose methods of inducting grammars from this synthetic data and experiments to evaluate both the data and the ability of PCFGs to represent human activities.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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